Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi, Toktam; Rezaei, Niloofar; Fashtami, L Ataei; Totonchi, Mehdi

doi:10.1111/srt.12920

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…It is crucial to note that this study is limited by a scarcity of data resources and would benefit from further validation with more extensive datasets. Khatibi T et al [78] presented a method for locating vitiligo lesions in skin images using the Stack Ensemble of Deep and Conventional Image Segmentation (SEDCIS) method for unsupervised stack integration. This localization of vitiligo lesions facilitates precise segmentation and evaluation of their surfaces.…”

Section: Deep Learningmentioning

confidence: 99%

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

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Section: Deep Learningmentioning

confidence: 99%

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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“…Dermoscopy is used to collect photographs of the skin, while a biopsy and a microscope are required to obtain images of other medical structures. 14 15,16 and other deep learning algorithms [17][18][19][20][21][22] have shown great progress in various skin cancer classifications, with excellent accuracy and robustness. Extraction of discriminative features from photos of skin lesions has been used to obtain outstanding classification results using DenseNet, 23 InceptionNet, 24 and ResNet.…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty of differentiating malignant from benign skin lesions has prompted the development of several cutting‐edge techniques. Convolutional Neural Networks (CNNs) 15,16 and other deep learning algorithms 17–22 have shown great progress in various skin cancer classifications, with excellent accuracy and robustness. Extraction of discriminative features from photos of skin lesions has been used to obtain outstanding classification results using DenseNet, 23 InceptionNet, 24 and ResNet 25 .…”

Section: Introductionmentioning

confidence: 99%

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Akram,

Rashid,

Jaffar

et al. 2023

Skin Research and Technology

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IntroductionParticularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.MethodThis research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset.ResultsThe experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well.ConclusionIn conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

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“…Validity was evaluated in 14 studies and was based on a comparison to a second instrument. Five were classified as adequate (contact planimetry: n = 3; 2D DIAS: n = 2), while 8 studies (57%) received an inadequate score (Aydin et al, 2007; Toh et al, 2018; Uitentuis et al, 2020; Van Geel et al, 2004; van Geel, Vandendriessche, et al, 2019) (Hayashi et al, 2016; Kanthraj et al, 1997; Khatibi et al, 2021; Kislal & Halasz, 2013; Kohli et al, 2015; Marrakchi et al, 2008; Nugroho et al, 2013; Sheth et al, 2015). Inadequate ratings were due to low statistical evidence and/or weak reliability of the comparator (no measurement properties evaluated for the comparison instrument).…”

Section: Resultsmentioning

confidence: 99%

Image analysis systems to calculate the surface area of vitiligo lesions: A systematic review of measurement properties

Geel

Saeys

Causenbroeck

et al. 2022

Pigment Cell Melanoma Res

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Several digital image analysis systems have been developed for surface calculation of vitiligo lesions. Critical assessment of their measurement properties is crucial to support evidence‐based recommendations on the most suitable instruments and will reveal the need for future research. A systematic review was performed to systematically summarize, compare, and critically assess the measurement properties of digital and analogue analysis systems for surface calculation of vitiligo lesions following the Consensus‐Based Standards for the Selection of Health Measurement Instruments (COSMIN) recommendations. Nineteen clinical trials were selected including 25 different instruments. Manual tracing on transparent sheets (contact planimetry) combined with digital measurement or point counting can be considered as the best validated method for the evaluation of target lesions taking into account the skin curvatures. Two‐dimensional digital imaging analysis on photographs seems also robust although confirmatory data of different research groups using the same digital instrument in a wide range of skin types are missing. Analysis based on 3D photography is still in its early stage but is promising for whole‐body analysis. However, the reported data on the quality of the instruments for surface area calculation of vitiligo lesions were in general rather limited. Therefore, future high‐quality validation studies are required also including full body evaluations.

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Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Cited by 11 publications

References 37 publications

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Image analysis systems to calculate the surface area of vitiligo lesions: A systematic review of measurement properties

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